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<a href="_hypervolume_contribution_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * </span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \brief Implements the frontend for the HypervolumeContribution algorithms, including the approximations</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> *</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> *</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * \author     O.Krause</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * \date        2014-2016</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> *</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * </span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * </span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * (at your option) any later version.</span></div>
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<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
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<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> *</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> */</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_DIRECTSEARCH_HYPERVOLUMECONTRIBUTION_H</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="preprocessor">#define SHARK_ALGORITHMS_DIRECTSEARCH_HYPERVOLUMECONTRIBUTION_H</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span> </div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="preprocessor">#include &lt;<a class="code" href="_hypervolume_contribution2_d_8h.html">shark/Algorithms/DirectSearch/Operators/Hypervolume/HypervolumeContribution2D.h</a>&gt;</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#include &lt;<a class="code" href="_hypervolume_contribution3_d_8h.html">shark/Algorithms/DirectSearch/Operators/Hypervolume/HypervolumeContribution3D.h</a>&gt;</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#include &lt;<a class="code" href="_hypervolume_contribution_m_d_8h.html">shark/Algorithms/DirectSearch/Operators/Hypervolume/HypervolumeContributionMD.h</a>&gt;</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#include &lt;<a class="code" href="_hypervolume_contribution_approximator_8h.html">shark/Algorithms/DirectSearch/Operators/Hypervolume/HypervolumeContributionApproximator.h</a>&gt;</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span> </div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {<span class="comment"></span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment">/// \brief Frontend for hypervolume contribution algorithms in m dimensions.</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">///</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment">///  Depending on the dimensionality of the problem, one of the specialized algorithms is called.</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">///  For large dimensionalities for which there are no specialized fast algorithms,</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">///  the exponential time algorithm is called. </span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">///  Also a log-transformation of points is supported</span></div>
<div class="foldopen" id="foldopen00047" data-start="{" data-end="};">
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno"><a class="line" href="structshark_1_1_hypervolume_contribution.html">   47</a></span><span class="comment"></span><span class="keyword">struct </span><a class="code hl_struct" href="structshark_1_1_hypervolume_contribution.html" title="Frontend for hypervolume contribution algorithms in m dimensions.">HypervolumeContribution</a> {</div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment"></span> </div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">    /// \brief Default c&#39;tor.</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno"><a class="line" href="structshark_1_1_hypervolume_contribution.html#ab804bcf6ac35e60fcfae484ce3b30b34">   50</a></span><span class="comment"></span>    <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#ab804bcf6ac35e60fcfae484ce3b30b34" title="Default c&#39;tor.">HypervolumeContribution</a>() : m_useApproximation(false) {}</div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>    <span class="comment"></span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">    ///\brief True if the hypervolume approximation is to be used in dimensions &gt; 3.</span></div>
<div class="foldopen" id="foldopen00053" data-start="{" data-end="}">
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"><a class="line" href="structshark_1_1_hypervolume_contribution.html#a29198ff8cda59711f36122db30a39413">   53</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#a29198ff8cda59711f36122db30a39413" title="True if the hypervolume approximation is to be used in dimensions &gt; 3.">useApproximation</a>(<span class="keywordtype">bool</span> <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#a29198ff8cda59711f36122db30a39413" title="True if the hypervolume approximation is to be used in dimensions &gt; 3.">useApproximation</a>){</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>        m_useApproximation = <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#a29198ff8cda59711f36122db30a39413" title="True if the hypervolume approximation is to be used in dimensions &gt; 3.">useApproximation</a>;</div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    }</div>
</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    </div>
<div class="foldopen" id="foldopen00057" data-start="{" data-end="}">
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"><a class="line" href="structshark_1_1_hypervolume_contribution.html#a256082b054ca17a3bf55857dc0513975">   57</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#a256082b054ca17a3bf55857dc0513975">approximationEpsilon</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>        <span class="keywordflow">return</span> m_approximationAlgorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution_approximator.html#aaa63c8502accc68b992534217eb32169">epsilon</a>();</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    }</div>
</div>
<div class="foldopen" id="foldopen00060" data-start="{" data-end="}">
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"><a class="line" href="structshark_1_1_hypervolume_contribution.html#a3b68674011cf878a1c8e0053c82aa2e7">   60</a></span>    <span class="keywordtype">double</span>&amp; <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#a3b68674011cf878a1c8e0053c82aa2e7">approximationEpsilon</a>(){</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>        <span class="keywordflow">return</span> m_approximationAlgorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution_approximator.html#aaa63c8502accc68b992534217eb32169">epsilon</a>();</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>    }</div>
</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>    </div>
<div class="foldopen" id="foldopen00064" data-start="{" data-end="}">
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"><a class="line" href="structshark_1_1_hypervolume_contribution.html#ad69390eb1fc2b938ab7c4e75b29a07de">   64</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#ad69390eb1fc2b938ab7c4e75b29a07de">approximationDelta</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        <span class="keywordflow">return</span> m_approximationAlgorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution_approximator.html#a57c159dc8900f273d0f0fd58147ee6d4">delta</a>();</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>    }</div>
</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    </div>
<div class="foldopen" id="foldopen00068" data-start="{" data-end="}">
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"><a class="line" href="structshark_1_1_hypervolume_contribution.html#ab9c0d9bfbfd0a7290d1dad26f6b1a7c3">   68</a></span>    <span class="keywordtype">double</span>&amp; <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#ab9c0d9bfbfd0a7290d1dad26f6b1a7c3">approximationDelta</a>(){</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>        <span class="keywordflow">return</span> m_approximationAlgorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution_approximator.html#a57c159dc8900f273d0f0fd58147ee6d4">delta</a>();</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>    }</div>
</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>    </div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>    <span class="keyword">template</span>&lt;<span class="keyword">typename</span> Archive&gt;</div>
<div class="foldopen" id="foldopen00073" data-start="{" data-end="}">
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"><a class="line" href="structshark_1_1_hypervolume_contribution.html#a9bc3edc57e78e822481b4a20a93e33f9">   73</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#a9bc3edc57e78e822481b4a20a93e33f9">serialize</a>( Archive &amp; archive, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> version ) {</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>        archive &amp; BOOST_SERIALIZATION_NVP(m_useApproximation);</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        archive &amp; BOOST_SERIALIZATION_NVP(m_approximationAlgorithm);</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>    }</div>
</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>    <span class="comment"></span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment">    /// \brief Returns the index of the points with smallest contribution as well as their contribution.</span></div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment">    /// \param [in] points The set \f$S\f$ of points from which to select the smallest contributor.</span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment">    /// \param [in] k The number of points to select.</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment">    /// \param [in] ref The reference Point\f$\vec{r} \in \mathbb{R}^2\f$ for the hypervolume calculation, needs to fulfill: \f$ \forall s \in S: s \preceq \vec{r}\f$.</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Set, <span class="keyword">typename</span> VectorType&gt;</div>
<div class="foldopen" id="foldopen00084" data-start="{" data-end="}">
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"><a class="line" href="structshark_1_1_hypervolume_contribution.html#a46626da682183658b940865fd4589506">   84</a></span>    std::vector&lt;KeyValuePair&lt;double,std::size_t&gt; &gt; <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#a46626da682183658b940865fd4589506" title="Returns the index of the points with smallest contribution as well as their contribution.">smallest</a>(Set <span class="keyword">const</span>&amp; points, std::size_t k, <a class="code hl_typedef" href="_c_svm_linear_8cpp.html#ab106d665148183a2dc94dcf8716c9203">VectorType</a> <span class="keyword">const</span>&amp; ref)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(points.size() &gt;= k, <span class="stringliteral">&quot;There must be at least k points in the set&quot;</span>);</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>( points.begin()-&gt;size() == ref.size() );</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>        std::size_t numObjectives = ref.size();</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>        <span class="keywordflow">if</span>(numObjectives == 2){</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>            <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution2_d.html" title="Finds the smallest/largest Contributors given 2D points.">HypervolumeContribution2D</a> algorithm;</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>            <span class="keywordflow">return</span> algorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution2_d.html#a414e72cf54c537bd456e50a733ef236a" title="Returns the index of the points with smallest contribution.">smallest</a>(points, k, ref);</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>        }<span class="keywordflow">else</span> <span class="keywordflow">if</span>(numObjectives == 3){</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>            <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution3_d.html" title="Finds the hypervolume contribution for points in 3DD.">HypervolumeContribution3D</a> algorithm;</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>            <span class="keywordflow">return</span> algorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution3_d.html#ac4d761c1d24708031664aff96129c043" title="Returns the index of the points with smallest contribution as well as their contribution.">smallest</a>(points, k, ref);</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        }<span class="keywordflow">else</span> <span class="keywordflow">if</span>(m_useApproximation){</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>            <span class="keywordflow">return</span> m_approximationAlgorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution_approximator.html#a5d9f9832db56f043a2f389ad5a386b55" title="Determines the point contributing the least hypervolume to the overall set of points.">smallest</a>(points, k, ref);</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>        }<span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>            <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution_m_d.html" title="Finds the hypervolume contribution for points in MD.">HypervolumeContributionMD</a> algorithm;</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>            <span class="keywordflow">return</span> algorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution_m_d.html#acfeefc0f057455a384e88070a9383e5e" title="Returns the index of the points with smallest contribution.">smallest</a>(points, k, ref);</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>        }</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>    }</div>
</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>    <span class="comment"></span></div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span><span class="comment">    /// \brief Returns the index of the points with largest contribution as well as their contribution.</span></div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment">    /// \param [in] points The set \f$S\f$ of points from which to select the largest contributor.</span></div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment">    /// \param [in] k Number of points.</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="comment">    /// \param [in] ref The reference Point\f$\vec{r} \in \mathbb{R}^2\f$ for the hypervolume calculation, needs to fulfill: \f$ \forall s \in S: s \preceq \vec{r}\f$.</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Set, <span class="keyword">typename</span> VectorType&gt;</div>
<div class="foldopen" id="foldopen00108" data-start="{" data-end="}">
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"><a class="line" href="structshark_1_1_hypervolume_contribution.html#ad7aba98bf20f20f16135b2e2f3b5ea2b">  108</a></span>    std::vector&lt;KeyValuePair&lt;double,std::size_t&gt; &gt; <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#ad7aba98bf20f20f16135b2e2f3b5ea2b" title="Returns the index of the points with largest contribution as well as their contribution.">largest</a>(Set <span class="keyword">const</span>&amp; points, std::size_t k, <a class="code hl_typedef" href="_c_svm_linear_8cpp.html#ab106d665148183a2dc94dcf8716c9203">VectorType</a> <span class="keyword">const</span>&amp; ref)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(points.size() &gt;= k, <span class="stringliteral">&quot;There must be at least k points in the set&quot;</span>);</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>( points.begin()-&gt;size() == ref.size() );</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        std::size_t numObjectives = ref.size();</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>        <span class="keywordflow">if</span>(numObjectives == 2){</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>            <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution2_d.html" title="Finds the smallest/largest Contributors given 2D points.">HypervolumeContribution2D</a> algorithm;</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>            <span class="keywordflow">return</span> algorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution2_d.html#a28762ad8d869ae3549fe4f63394de105" title="Returns the index of the points with largest contribution.">largest</a>(points, k, ref);</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        }<span class="keywordflow">else</span> <span class="keywordflow">if</span>(numObjectives == 3){</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>            <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution3_d.html" title="Finds the hypervolume contribution for points in 3DD.">HypervolumeContribution3D</a> algorithm;</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>            <span class="keywordflow">return</span> algorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution3_d.html#a21b543685d2c16e733c932f8ca4f41d9" title="Returns the index of the points with largest contribution as well as their contribution.">largest</a>(points, k, ref);</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>        }<span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>            <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(!m_useApproximation, <span class="stringliteral">&quot;Largest not implemented for approximation algorithm&quot;</span>);</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>            <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution_m_d.html" title="Finds the hypervolume contribution for points in MD.">HypervolumeContributionMD</a> algorithm;</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>            <span class="keywordflow">return</span> algorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution_m_d.html#acaab1a44fb4b791251f0fbfaa69bb55d" title="Returns the index of the points with largest contribution.">largest</a>(points, k, ref);</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        }</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>    }</div>
</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment"></span> </div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span><span class="comment">    /// \brief Returns the index of the points with smallest contribution as well as their contribution.</span></div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span><span class="comment">    /// As no reference point is given, the extremum points can not be computed and are never selected.</span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment">    /// \param [in] points The set \f$S\f$ of points from which to select the smallest contributor.</span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment">    /// \param [in] k The number of points to select.</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Set&gt;</div>
<div class="foldopen" id="foldopen00132" data-start="{" data-end="}">
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"><a class="line" href="structshark_1_1_hypervolume_contribution.html#acbf96ebbdb6fb438fb2efe64b235967d">  132</a></span>    std::vector&lt;KeyValuePair&lt;double,std::size_t&gt; &gt; <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#acbf96ebbdb6fb438fb2efe64b235967d" title="Returns the index of the points with smallest contribution as well as their contribution.">smallest</a>(Set <span class="keyword">const</span>&amp; points, std::size_t k)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(points.size() &gt;= k, <span class="stringliteral">&quot;There must be at least k points in the set&quot;</span>);</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        std::size_t numObjectives = points[0].size();</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        <span class="keywordflow">if</span>(numObjectives == 2){</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>            <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution2_d.html" title="Finds the smallest/largest Contributors given 2D points.">HypervolumeContribution2D</a> algorithm;</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>            <span class="keywordflow">return</span> algorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution2_d.html#a414e72cf54c537bd456e50a733ef236a" title="Returns the index of the points with smallest contribution.">smallest</a>(points, k);</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        }<span class="keywordflow">else</span> <span class="keywordflow">if</span>(numObjectives == 3){</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>            <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution3_d.html" title="Finds the hypervolume contribution for points in 3DD.">HypervolumeContribution3D</a> algorithm;</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>            <span class="keywordflow">return</span> algorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution3_d.html#ac4d761c1d24708031664aff96129c043" title="Returns the index of the points with smallest contribution as well as their contribution.">smallest</a>(points, k);</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>        }<span class="keywordflow">else</span> <span class="keywordflow">if</span>(m_useApproximation){</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>            <span class="keywordflow">return</span> m_approximationAlgorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution_approximator.html#a5d9f9832db56f043a2f389ad5a386b55" title="Determines the point contributing the least hypervolume to the overall set of points.">smallest</a>(points, k);</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        }<span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>            <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution_m_d.html" title="Finds the hypervolume contribution for points in MD.">HypervolumeContributionMD</a> algorithm;</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>            <span class="keywordflow">return</span> algorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution_m_d.html#acfeefc0f057455a384e88070a9383e5e" title="Returns the index of the points with smallest contribution.">smallest</a>(points, k);</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>        }</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>    }</div>
</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>    <span class="comment"></span></div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span><span class="comment">    /// \brief Returns the index of the points with largest contribution as well as their contribution.</span></div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span><span class="comment">    /// As no reference point is given, the extremum points can not be computed and are never selected.</span></div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span><span class="comment">    /// \param [in] points The set \f$S\f$ of points from which to select the smallest contributor.</span></div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span><span class="comment">    /// \param [in] k The number of points to select.</span></div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Set&gt;</div>
<div class="foldopen" id="foldopen00156" data-start="{" data-end="}">
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"><a class="line" href="structshark_1_1_hypervolume_contribution.html#aeee807833c090414b2c6dc41c1f8eca6">  156</a></span>    std::vector&lt;KeyValuePair&lt;double,std::size_t&gt; &gt; <a class="code hl_function" href="structshark_1_1_hypervolume_contribution.html#aeee807833c090414b2c6dc41c1f8eca6" title="Returns the index of the points with largest contribution as well as their contribution.">largest</a>(Set <span class="keyword">const</span>&amp; points, std::size_t k)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(points.size() &gt;= k, <span class="stringliteral">&quot;There must be at least k points in the set&quot;</span>);</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        std::size_t numObjectives = points[0].size();</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        <span class="keywordflow">if</span>(numObjectives == 2){</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>            <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution2_d.html" title="Finds the smallest/largest Contributors given 2D points.">HypervolumeContribution2D</a> algorithm;</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>            <span class="keywordflow">return</span> algorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution2_d.html#a28762ad8d869ae3549fe4f63394de105" title="Returns the index of the points with largest contribution.">largest</a>(points, k);</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        }<span class="keywordflow">else</span> <span class="keywordflow">if</span>(numObjectives == 3){</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>            <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution3_d.html" title="Finds the hypervolume contribution for points in 3DD.">HypervolumeContribution3D</a> algorithm;</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>            <span class="keywordflow">return</span> algorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution3_d.html#a21b543685d2c16e733c932f8ca4f41d9" title="Returns the index of the points with largest contribution as well as their contribution.">largest</a>(points, k);</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        }<span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>            <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(!m_useApproximation, <span class="stringliteral">&quot;Largest not implemented for approximation algorithm&quot;</span>);</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>            <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution_m_d.html" title="Finds the hypervolume contribution for points in MD.">HypervolumeContributionMD</a> algorithm;</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>            <span class="keywordflow">return</span> algorithm.<a class="code hl_function" href="structshark_1_1_hypervolume_contribution_m_d.html#acaab1a44fb4b791251f0fbfaa69bb55d" title="Returns the index of the points with largest contribution.">largest</a>(points, k);</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        }</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>    }</div>
</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span> </div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>    <span class="keywordtype">bool</span> m_useApproximation;</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>    <a class="code hl_struct" href="structshark_1_1_hypervolume_contribution_approximator.html" title="Approximately determines the point of a set contributing the least hypervolume.">HypervolumeContributionApproximator</a> m_approximationAlgorithm;</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>};</div>
</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span> </div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>}</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span><span class="preprocessor">#endif</span></div>
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